(Consider what would happen to the memory addresses accessed by the second, third, and subsequent thread blocks if the thread block size was not a multiple of warp size, for example.). Moreover, in such cases, the argument-reduction code uses local memory, which can affect performance even more because of the high latency of local memory. As a result, a read from constant memory costs one memory read from device memory only on a cache miss; otherwise, it just costs one read from the constant cache. Reproduction of information in this document is permissible only if approved in advance by NVIDIA in writing, reproduced without alteration and in full compliance with all applicable export laws and regulations, and accompanied by all associated conditions, limitations, and notices. Memory optimizations are the most important area for performance. Comparing Synchronous vs Asynchronous Copy from Global Memory to Shared Memory. Data Transfer Between Host and Device provides further details, including the measurements of bandwidth between the host and the device versus within the device proper. So while the impact is still evident it is not as large as we might have expected. -use_fast_math compiler option of nvcc coerces every functionName() call to the equivalent __functionName() call. Memory Access Although it is also possible to synchronize the CPU thread with a particular stream or event on the GPU, these synchronization functions are not suitable for timing code in streams other than the default stream. That is, a thread can safely read a memory location via texture if the location has been updated by a previous kernel call or memory copy, but not if it has been previously updated by the same thread or another thread within the same kernel call. Functions following the __functionName() naming convention map directly to the hardware level. If all threads of a warp access the same location, then constant memory can be as fast as a register access. Threads with a false predicate do not write results, and also do not evaluate addresses or read operands. The following examples use the cuBLAS library from CUDA Toolkit 5.5 as an illustration: In a shared library on Linux, there is a string field called the SONAME that indicates the binary compatibility level of the library. The results are shown in the chart below, where we see good performance regardless of whether the persistent data fits in the L2 set-aside or not. However, this approach of determining how register count affects occupancy does not take into account the register allocation granularity. Intermediate data structures should be created in device memory, operated on by the device, and destroyed without ever being mapped by the host or copied to host memory. Concurrent copy and execute demonstrates how to overlap kernel execution with asynchronous data transfer. In reality, most applications do not exhibit perfectly linear strong scaling, even if they do exhibit some degree of strong scaling. Low Priority: Avoid automatic conversion of doubles to floats. Within a kernel call, the texture cache is not kept coherent with respect to global memory writes, so texture fetches from addresses that have been written via global stores in the same kernel call return undefined data. Local memory is used only to hold automatic variables. If the GPU must wait on one warp of threads, it simply begins executing work on another. Access to shared memory is much faster than global memory access because it is located on chip. Medium Priority: Prefer faster, more specialized math functions over slower, more general ones when possible. The types of operations are an additional factor, as additions have different complexity profiles than, for example, trigonometric functions. It is also the only way for applications to run on devices that did not exist at the time the application was compiled. Consequently, the order in which arithmetic operations are performed is important. This new feature is exposed via the pipeline API in CUDA. In this particular example, the offset memory throughput achieved is, however, approximately 9/10th, because adjacent warps reuse the cache lines their neighbors fetched. Texture references that are bound to CUDA arrays can be written to via surface-write operations by binding a surface to the same underlying CUDA array storage). To assist with this, the CUDA Driver API provides methods to access and manage a special context on each GPU called the primary context. The new Tensor Cores use a larger base matrix size and add powerful new math modes including: Support for FP64 Tensor Core, using new DMMA instructions. A key concept in this effort is occupancy, which is explained in the following sections. In the previous post, I looked at how global memory accesses by a group of threads can be coalesced into a single transaction, and howalignment and stride affect coalescing for various generations of CUDA hardware. (Note that the CUDA compiler considers any device code that does not contribute to a write to global memory as dead code subject to elimination, so we must at least write something out to global memory as a result of our addressing logic in order to successfully apply this strategy.). We define source compatibility as a set of guarantees provided by the library, where a well-formed application built against a specific version of the library (using the SDK) will continue to build and run without errors when a newer version of the SDK is installed. outside your established ABI contract. Missing dependencies is also a binary compatibility break, hence you should provide fallbacks or guards for functionality that depends on those interfaces. This variant simply uses the transpose of A in place of B, so C = AAT. Managing your GPU cluster will help achieve maximum GPU utilization and help you and your users extract the best possible performance. CUDA Compatibility Developers Guide, 15.3.1. Randomly accessing. The performance of the sliding-window benchmark with fixed hit-ratio of 1.0. Shared memory can be thought of as a software-controlled cache on the processor - each Streaming Multiprocessor has a small amount of shared memory (e.g. OpenCL is a trademark of Apple Inc. used under license to the Khronos Group Inc. NVIDIA and the NVIDIA logo are trademarks or registered trademarks of NVIDIA Corporation in the U.S. and other countries. The warp size is 32 threads and the number of banks is also 32, so bank conflicts can occur between any threads in the warp. Not the answer you're looking for? Before we proceed further on this topic, its important for developers to understand the concept of Minimum Driver Version and how that may affect them. The CUDA software environment consists of three parts: CUDA Toolkit (libraries, CUDA runtime and developer tools) - SDK for developers to build CUDA applications. The NVIDIA A100 GPU supports shared memory capacity of 0, 8, 16, 32, 64, 100, 132 or 164 KB per SM. Detecting Hardware and Software Configuration. These recommendations are categorized by priority, which is a blend of the effect of the recommendation and its scope. A system with multiple GPUs may contain GPUs of different hardware versions and capabilities. Shared memory can also be used to avoid uncoalesced memory accesses by loading and storing data in a coalesced pattern from global memory and then reordering it in shared memory. Code that cannot be sufficiently parallelized should run on the host, unless doing so would result in excessive transfers between the host and the device. On devices that are capable of concurrent copy and compute, it is possible to overlap kernel execution on the device with data transfers between the host and the device. The texture cache is optimized for 2D spatial locality, so threads of the same warp that read texture addresses that are close together will achieve best performance. Hardware utilization can also be improved in some cases by designing your application so that multiple, independent kernels can execute at the same time. 2) In one block I need to load into shared memory the queues of other blocks. As mentioned in Occupancy, higher occupancy does not always equate to better performance. Page-locked memory mapping is enabled by calling cudaSetDeviceFlags() with cudaDeviceMapHost. CUDA kernel and thread hierarchy Copy the results from device memory to host memory, also called device-to-host transfer. For further details on the programming features discussed in this guide, please refer to the CUDA C++ Programming Guide. In the example above, we can clearly see that the function genTimeStep() takes one-third of the total running time of the application. If textures are fetched using tex1D(),tex2D(), or tex3D() rather than tex1Dfetch(), the hardware provides other capabilities that might be useful for some applications such as image processing, as shown in Table 4. Avoid long sequences of diverged execution by threads within the same warp. In addition to the calculator spreadsheet, occupancy can be determined using the NVIDIA Nsight Compute Profiler. What is CUDA memory? - Quora With the CUDA Driver API, a CUDA application process can potentially create more than one context for a given GPU. Load the GPU program and execute, caching data on-chip for performance. Is it known that BQP is not contained within NP? Devices of compute capability 1.0 to 1.3 have 16 KB/Block, compute 2.0 onwards have 48 KB/Block shared memory by default. These exceptions, which are detailed in Features and Technical Specifications of the CUDA C++ Programming Guide, can lead to results that differ from IEEE 754 values computed on the host system. (Developers targeting a single machine with known configuration may choose to skip this section.). Between 128 and 256 threads per block is a good initial range for experimentation with different block sizes. Copyright 2007-2023, NVIDIA Corporation & Affiliates. When the latter is much lower than the former, design or implementation details are likely to reduce bandwidth, and it should be the primary goal of subsequent optimization efforts to increase it. Optimizing memory usage starts with minimizing data transfers between the host and the device because those transfers have much lower bandwidth than internal device data transfers. The high-priority recommendations from those guides are as follows: Find ways to parallelize sequential code. --ptxas-options=-v or -Xptxas=-v lists per-kernel register, shared, and constant memory usage. A pointer to a structure with a size embedded is a better solution. It is easy and informative to explore the ramifications of misaligned accesses using a simple copy kernel, such as the one in A copy kernel that illustrates misaligned accesses. If an appropriate native binary (cubin) is not available, but the intermediate PTX code (which targets an abstract virtual instruction set and is used for forward-compatibility) is available, then the kernel will be compiled Just In Time (JIT) (see Compiler JIT Cache Management Tools) from the PTX to the native cubin for the device. Sample CUDA configuration data reported by deviceQuery. Results obtained using double-precision arithmetic will frequently differ from the same operation performed via single-precision arithmetic due to the greater precision of the former and due to rounding issues. For more details on the new Tensor Core operations refer to the Warp Matrix Multiply section in the CUDA C++ Programming Guide. Because execution within a stream occurs sequentially, none of the kernels will launch until the data transfers in their respective streams complete. NVIDIA shall have no liability for the consequences or use of such information or for any infringement of patents or other rights of third parties that may result from its use. As a result, Thrust can be utilized in rapid prototyping of CUDA applications, where programmer productivity matters most, as well as in production, where robustness and absolute performance are crucial. The PTX string generated by NVRTC can be loaded by cuModuleLoadData and cuModuleLoadDataEx. To keep the kernels simple, M and N are multiples of 32, since the warp size (w) is 32 for current devices. For a warp of threads, col represents sequential columns of the transpose of A, and therefore col*TILE_DIM represents a strided access of global memory with a stride of w, resulting in plenty of wasted bandwidth. How to manage this resource utilization is discussed in the final sections of this chapter. For example, the ability to overlap kernel execution with asynchronous data transfers between the host and the device is available on most but not all GPUs irrespective of the compute capability. A noteworthy exception to this are completely random memory access patterns. To do this, the simpleMultiply kernel (Unoptimized matrix multiplication) calculates the output elements of a tile of matrix C. In Unoptimized matrix multiplication, a, b, and c are pointers to global memory for the matrices A, B, and C, respectively; blockDim.x, blockDim.y, and TILE_DIM are all equal to w. Each thread in the wxw-thread block calculates one element in a tile of C. row and col are the row and column of the element in C being calculated by a particular thread. Therefore, to accurately measure the elapsed time for a particular call or sequence of CUDA calls, it is necessary to synchronize the CPU thread with the GPU by calling cudaDeviceSynchronize() immediately before starting and stopping the CPU timer. (See Data Transfer Between Host and Device.) Misaligned sequential addresses that fall within five 32-byte segments, Memory allocated through the CUDA Runtime API, such as via cudaMalloc(), is guaranteed to be aligned to at least 256 bytes. The two kernels are very similar, differing only in how the shared memory arrays are declared and how the kernels are invoked. Choosing execution parameters is a matter of striking a balance between latency hiding (occupancy) and resource utilization. CUDA supports several compatibility choices: First introduced in CUDA 10, the CUDA Forward Compatible Upgrade is designed to allow users to get access to new CUDA features and run applications built with new CUDA releases on systems with older installations of the NVIDIA datacenter driver. For purposes of calculating occupancy, the number of registers used by each thread is one of the key factors. New APIs can be added in minor versions. CUDA work occurs within a process space for a particular GPU known as a context. Before implementing lower priority recommendations, it is good practice to make sure all higher priority recommendations that are relevant have already been applied. The number of registers available, the maximum number of simultaneous threads resident on each multiprocessor, and the register allocation granularity vary over different compute capabilities. Floor returns the largest integer less than or equal to x. The compiler and hardware thread scheduler will schedule instructions as optimally as possible to avoid register memory bank conflicts. If not, my suggestion would be to start by breaking your work into separate kernels, and using the kernel launch(es) as sync points. Thread instructions are executed sequentially in CUDA, and, as a result, executing other warps when one warp is paused or stalled is the only way to hide latencies and keep the hardware busy. This kernel has an effective bandwidth of 144.4 GB/s on an NVIDIA Tesla V100. All rights reserved. The dimension and size of blocks per grid and the dimension and size of threads per block are both important factors. It would have been more so if adjacent warps had not exhibited such a high degree of reuse of the over-fetched cache lines. Shared memory enables cooperation between threads in a block. There's no way around this. Delays in rolling out new NVIDIA drivers could mean that users of such systems may not have access to new features available in CUDA releases. The same goes for other CUDA Toolkit libraries: cuFFT has an interface similar to that of FFTW, etc. The CUDA Toolkit Samples provide several helper functions for error checking with the various CUDA APIs; these helper functions are located in the samples/common/inc/helper_cuda.h file in the CUDA Toolkit. Having completed the GPU acceleration of one or more components of the application it is possible to compare the outcome with the original expectation. In many cases, the amount of shared memory required by a kernel is related to the block size that was chosen, but the mapping of threads to shared memory elements does not need to be one-to-one. Another important concept is the management of system resources allocated for a particular task. This context can be current to as many threads as desired within the creating process, and cuDevicePrimaryCtxRetain will fail if a non-primary context that was created with the CUDA driver API already exists on the device. Overall Performance Optimization Strategies, https://developer.nvidia.com/nsight-visual-studio-edition, https://developer.nvidia.com/debugging-solutions, https://developer.nvidia.com/content/precision-performance-floating-point-and-ieee-754-compliance-nvidia-gpus, Asynchronous and Overlapping Transfers with Computation, CUDA Driver API :: CUDA Toolkit Documentation, dynamically-linked version of the CUDA Runtime library, Where to Install Redistributed CUDA Libraries, https://developer.nvidia.com/gpu-deployment-kit, https://developer.nvidia.com/nvidia-management-library-nvml, https://developer.nvidia.com/cluster-management. For devices of compute capability 2.x, there are two settings, 48KBshared memory / 16KB L1 cache, and 16KB shared memory / 48KB L1 cache. Shared Memory in Matrix Multiplication (C=AAT), 9.2.3.4. High Priority: Use the effective bandwidth of your computation as a metric when measuring performance and optimization benefits. Functions following functionName() naming convention are slower but have higher accuracy (e.g., sinf(x) and expf(x)). For some architectures L1 and shared memory use same hardware and are configurable. The maximum number of concurrent warps per SM remains the same as in Volta (i.e., 64), and other factors influencing warp occupancy are: The register file size is 64K 32-bit registers per SM. If individual CUDA threads are copying elements of 16 bytes, the L1 cache can be bypassed. As an exception, scattered writes to HBM2 see some overhead from ECC but much less than the overhead with similar access patterns on ECC-protected GDDR5 memory. Having a semantically versioned ABI means the interfaces need to be maintained and versioned. They produce equivalent results. We cannot declare these directly, but small static allocations go . To allocate an array in shared memory we . Minimize data transfers between the host and the device. What is the difference between CUDA shared memory and global - Quora The cudaMemcpyAsync() function is a non-blocking variant of cudaMemcpy() in which control is returned immediately to the host thread. For devices of compute capability 2.0, the warp size is 32 threads and the number of banks is also 32. For example, to use only devices 0 and 2 from the system-wide list of devices, set CUDA_VISIBLE_DEVICES=0,2 before launching the application. The hardware splits a memory request that has bank conflicts into as many separate conflict-free requests as necessary, decreasing the effective bandwidth by a factor equal to the number of separate memory requests. In Using shared memory to improve the global memory load efficiency in matrix multiplication, each element in a tile of A is read from global memory only once, in a fully coalesced fashion (with no wasted bandwidth), to shared memory. Assess, Parallelize, Optimize, Deploy, 3.1.3.1. If this happens, the different execution paths must be executed separately; this increases the total number of instructions executed for this warp. This utility allows administrators to query GPU device state and, with the appropriate privileges, permits administrators to modify GPU device state. Sequential copy and execute and Staged concurrent copy and execute demonstrate this. Because separate registers are allocated to all active threads, no swapping of registers or other state need occur when switching among GPU threads. High Priority: Minimize the use of global memory. If the PTX is also not available, then the kernel launch will fail. For each iteration i of the for loop, the threads in a warp read a row of the B tile, which is a sequential and coalesced access for all compute capabilities. Using asynchronous copies does not use any intermediate register. For Windows, the /DELAY option is used; this requires that the application call SetDllDirectory() before the first call to any CUDA API function in order to specify the directory containing the CUDA DLLs. The effective bandwidth of this routine is 195.5 GB/s on an NVIDIA Tesla V100. Therefore, any memory load or store of n addresses that spans b distinct memory banks can be serviced simultaneously, yielding an effective bandwidth that is b times as high as the bandwidth of a single bank. Devices of compute capability 3.x allow a third setting of 32KB shared memory / 32KB L1 cache which can be obtained using the optioncudaFuncCachePreferEqual. Accesses to different addresses by threads within a warp are serialized, thus the cost scales linearly with the number of unique addresses read by all threads within a warp. So, if each thread block uses many registers, the number of thread blocks that can be resident on a multiprocessor is reduced, thereby lowering the occupancy of the multiprocessor. The combined L1 cache capacity for GPUs with compute capability 8.6 is 128 KB. In such cases, and when the execution time (tE) exceeds the transfer time (tT), a rough estimate for the overall time is tE + tT/nStreams for the staged version versus tE + tT for the sequential version. No contractual obligations are formed either directly or indirectly by this document. The interface is augmented to retrieve either the PTX or cubin if an actual architecture is specified. Floating Point Math Is not Associative, 8.2.3. The cubins are architecture-specific. Resources stay allocated to each thread until it completes its execution. Some recent Linux distributions enable automatic NUMA balancing (or AutoNUMA) by default. This illustrates the use of the shared memory as a user-managed cache when the hardware L1 cache eviction policy does not match up well with the needs of the application or when L1 cache is not used for reads from global memory. However, for each iteration i, all threads in a warp read the same value from global memory for matrix A, as the index row*TILE_DIM+i is constant within a warp. Asynchronous Copy from Global Memory to Shared Memory, 10. The way to avoid strided access is to use shared memory as before, except in this case a warp reads a row of A into a column of a shared memory tile, as shown in An optimized handling of strided accesses using coalesced reads from global memory. For example, consider the following code: Here, the sub-expression stride*i could overflow a 32-bit integer, so if i is declared as unsigned, the overflow semantics prevent the compiler from using some optimizations that might otherwise have applied, such as strength reduction. Instead, all instructions are scheduled, but a per-thread condition code or predicate controls which threads execute the instructions. No license, either expressed or implied, is granted under any NVIDIA patent right, copyright, or other NVIDIA intellectual property right under this document. Any CPU timer can be used to measure the elapsed time of a CUDA call or kernel execution. Tuning CUDA Applications for NVIDIA Ampere GPU Architecture. The context is explicit in the CUDA Driver API but is entirely implicit in the CUDA Runtime API, which creates and manages contexts automatically. The performance guidelines and best practices described in the CUDA C++ Programming Guide and the CUDA C++ Best Practices Guide apply to all CUDA-capable GPU architectures. Other peculiarities of floating-point arithmetic are presented in Features and Technical Specifications of the CUDA C++ Programming Guide as well as in a whitepaper and accompanying webinar on floating-point precision and performance available from https://developer.nvidia.com/content/precision-performance-floating-point-and-ieee-754-compliance-nvidia-gpus. (tens of kBs capacity) Global memory is main memory (GDDR,HBM, (1-32 GB)) and data is cached by L2,L1 caches. For slightly better performance, however, they should instead be declared as signed. The CUDA runtime has relaxed the minimum driver version check and thus no longer requires a driver upgrade when moving to a new minor release. Unified Shared Memory/L1/Texture Cache, NVIDIA Ampere GPU Architecture Compatibility Guide for CUDA Applications. These barriers can be used to implement fine grained thread controls, producer-consumer computation pipeline and divergence code patterns in CUDA. NVIDIA hereby expressly objects to applying any customer general terms and conditions with regards to the purchase of the NVIDIA product referenced in this document. The __pipeline_wait_prior(0) will wait until all the instructions in the pipe object have been executed. Although each of these instructions is scheduled for execution, only the instructions with a true predicate are actually executed. The NVIDIA nvcc compiler driver converts .cu files into C++ for the host system and CUDA assembly or binary instructions for the device. sorting the queues) and then a single threadblock would perform the clean-up tasks such as collecting the queues and processing in a single threadblock. Use several smaller thread blocks rather than one large thread block per multiprocessor if latency affects performance. This can be configured during runtime API from the host for all kernelsusing cudaDeviceSetCacheConfig() or on a per-kernel basis using cudaFuncSetCacheConfig(). The CUDA Driver API has a versioned C-style ABI, which guarantees that applications that were running against an older driver (for example CUDA 3.2) will still run and function correctly against a modern driver (for example one shipped with CUDA 11.0).

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